Instantly share code, notes, and snippets.

# Aidan RockeAidanRocke

• Sort options
Last active Feb 13, 2018
View conditional_dist.ipynb Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Created Mar 14, 2018
approximating a uniform distribution with a normal distribution
View normal_approximation
 """ Created on Tue Mar 13 19:17:39 2018 @author: aidanrocke """ import tensorflow as tf import numpy as np
Last active May 9, 2018
Minimal example of accumulated gradients in TensorFlow Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Created Jun 8, 2018 — forked from kingjr/google_translate_challenge.py
Find simplest google translate request that generate the maximum number of unique words
 from googletrans import Translator from itertools import product from pandas import DataFrame, read_csv import numpy as np import string import time import os # Get google translator object translator = Translator()
Created Aug 20, 2018 Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Created Nov 29, 2018
 #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Nov 29 13:06:26 2018 @author: aidanrockea """ import numpy as np import tensorflow as tf import tensorflow_probability as tfp
Last active Mar 6, 2019
A test for local convexity
View local_convex_test.jl
 using LinearAlgebra using Random function local_convex_test(N) #srand(1234) sum = 0.0
Last active Mar 11, 2019
View circuit_complexity.jl
 using LinearAlgebra using Random function circuit_complexity(N) radii = zeros(1,N); for i=1:N
Last active Mar 27, 2019
A generative model based on the 2/3 power law
View crazy_paths.jl
 using Random function crazy_paths(N,delta_t) ddx, dx = 10*(2*rand(N+1) .-1.0), 10*(2*rand(N+1) .-1.0) ddy, dy = 10*(2*rand(N+1) .-1.0), 10*(2*rand(N+1) .-1.0) x, y = 10*(2*rand(N+1) .-1.0), 10*(2*rand(N+1) .-1.0)
Created Jun 14, 2019
View monotone_approx.jl
 using Distributions using Statistics function monotone_approx(N::Int64,n_trials::Int64,n::Int64) """ inputs: N: the range of U([-N,N]) n_trials: the number of times we generate random vectors n: the 'dimension' of the random vector
You can’t perform that action at this time.